[Project Addition] PCOS Disease Detection

[me-shweta]

Deep Learning Simplified Repository (Proposing new issue)

⭐ Detect PCOS using ML :
⭐ CNN Architecture to Detect PCOS from Ovarian Ultrasound Images and and Statistical Data :
⭐ Dataset :

1. The dataset "PCOS detection using ultrasound images" available on Kaggle. Size: The dataset contains a total of 2,000 ovarian ultrasound images, with 787 infected and 1145 normal images. Data folder consist of 'train' and 'test' subfolders containing 2 categories of data 'infected' and 'notinfected' infected : Images of ovaries having PCOS notinfected : Images of healthy ovaries

   https://www.kaggle.com/datasets/anaghachoudhari/pcos-detection-using-ultrasound-images

⭐ Approach :

• Data Preprocessing
• Feature Extraction
• Algorithm Development
• Validation and Performance Assessment

---

📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

---

Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

---

:white_check_mark: To be Mentioned while taking the issue :

• Full name : Shweta Meena
• GitHub Profile Link : https://github.com/me-shweta
• Email ID : shwetameena0511@gmail.com
• Participant ID (if applicable): me-shweta
• Approach for this Project : Detection of PCOS
• What is your participant role? (Mention the Open Source program): GSSoC 2024 Contributor

---

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions[bot]]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[abhisheks008]

Can you please share the approach for solving this issue with the deep learning models?

[me-shweta]

@abhisheks008 My way of tackling this problem is straightforward. I carefully split datasets to keep things balanced and make sure data preprocessing is efficient by using techniques like caching, shuffling, and prefetching. Then, I set up neural network called Convolutional Neural Networks (CNNs) for sorting images into categories. I train the model by putting it through many rounds of learning using a method called the Adam optimizer. Checking how well it's doing is easy with graphs that show its progress. After that, I test it with some pictures and make sure it's working smoothly.

[abhisheks008]

You need to implement at least 3-4 neural network architectures for this dataset. Are you uo for this?

[me-shweta]

@abhisheks008 Yes! I will give it my best, so I'm up for this.

[abhisheks008]

Assigned @me-shweta